Dietary intakes of patients with alcohol use disorder during a four-week protocol on an inpatient treatment unit found to meet Dietary Reference Intakes for macronutrients, but have variability in energy balance and adequacy of micronutrient intake

Abstract

Background

Despite literature supporting the importance of diet during rehabilitation, minimal research quantifies dietary intake during treatment for Alcohol Use Disorder (AUD).

Objective

To quantify dietary intake and energy balance of patients with AUD during inpatient treatment.

Design

This is a secondary analysis of data from a four-week observational protocol. Participants self-selected food from a room service menu. Dietary intake was recorded by patients and reviewed by nutrition staff. To quantify nutrient and food group intake, data were coded into Nutrition Data Systems for Research 2016–2017. Daily average intake was calculated for all dietary variables.

Participants/setting

Participants (N= 22) were adults seeking treatment for AUD at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017 and were enrolled in a study examining the microbiome during AUD rehabilitation. Four participants discontinued protocol participation prior to the 4^th study week and were not included in analyses examining change over time.

Main Outcome Measures

Main outcome measures included weight change, daily energy, macronutrient and select micronutrient intake.

Statistical analyses performed

Mean differences in intake and weight were assessed using non-parametric tests.

Results

Participants (64% male) were 46.3 ± 13.0 (mean ± SD) years old with a BMI of 23.9 ± 2.5 kg/m² and average intake of 2665 kcals per day, (45.9% carbohydrate, 34.9% fat and 19.1% protein). Eighty percent or more of this sample met the Estimated Average Requirement for 10 out of 16 micronutrients assessed. Males consumed more energy than estimated needs (p=0.003) and gained 2.67 ± 1.84 kg (p=0.006) when an outlier with weight loss and acute pancreatitis was removed from analysis. Females did not gain weight or consume more than estimated energy needs.

Conclusions

Overall macronutrient intake was within recommended ranges, but intake of other dietary components and weight gain was variable, supporting the need for individualized nutrition care during AUD treatment.

Introduction

Alcohol Use Disorder (AUD) is defined as “a medical condition characterized by an impaired ability to stop or control alcohol use despite adverse social, occupational or health consequences”.¹ The term AUD is a single disorder used in the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 to encompass two previous disorders of alcohol abuse and alcohol dependence that were used by DSM-IV.² AUD affects over 28 million people in the United States.³ Globally, alcohol misuse is the seventh-leading risk factor for premature death and disability. It has significant economic, physiological, psychological and metabolic consequences.⁴

Individuals with AUD are at heightened risk for malnutrition due to the displacement of nutritive sources of energy in the diet by alcohol and the reduced efficacy of digestion and absorption associated with the effects of alcohol on the digestive organs.⁵ Malnutrition is associated with increased morbidity and mortality; thus, assessment and intervention by a Registered Dietitian Nutritionist (RDN) is an important part of the interdisciplinary treatment for individuals with AUD.⁶ In addition to the role of diet in the correction of nutritional deficiencies, scientific advances over recent years also suggest multiple physiological pathways through which diet during AUD treatment may influence health and outcomes.^7–10 Despite a growing body of literature supporting the importance of diet during treatment, minimal recent research quantifies dietary intake during inpatient treatment for AUD.

While there is a paucity of published data that utilizes precise methods of dietary assessment during treatment for AUD, much of the literature that does focus on this population suggests that patients with AUD have poor diet quality during treatment, including excess intake of sugar, fat and energy and inadequate intake of fruits, vegetables and fiber.^11,12 However, findings are often extrapolated from reports associated with other substance use disorders (SUDs),^13–15 data indirectly addressing dietary intake, such as taste preference laboratory measures¹⁶ and subjective reports from patients and clinicians that focus on qualitative data reflecting diet.^14,15 Paradoxically, this literature also reports increased motivation to improve healthfulness of dietary choices among those recovering from SUDs.^9,14,15

Only two publications were found which utilized validated self-report instruments, either 24 hour recalls or food records, to quantify dietary intake among individuals undergoing AUD treatment programs and assess change throughout treatment. However, one of these studies assessed only 10 Latina and African American women in residential treatment centers¹⁷, while the other examined subjects at an outpatient treatment program in Denmark¹⁸. Thus, neither of these studies may reflect habits of men and women in the U.S. undergoing AUD treatment in hospital-based inpatient programs.

Imprecise measurement of diet, focus on very unique sub-populations of those with AUD and inconsistency in conclusions regarding dietary intake of those with AUD could lead to confusion regarding appropriate nutrition guidance and care of individuals undergoing treatment. The use of more precise methods to quantify dietary intake during inpatient treatment can help to inform clinicians and researchers about the actual food choices of patients undergoing treatment for AUD, rather than forcing a reliance on subjective or qualitative reports in the existing literature. Therefore, the objective of this analysis was to quantify dietary intake, including energy, macro- and micronutrient intake, and examine energy balance of participants diagnosed with AUD during participation in a four-week observational protocol within an inpatient hospital-based treatment program.

Methods

Study design and sample

Participants included in this analysis were enrolled in a four-week clinical observational study assessing the gut and oral microbiome in participants with AUD (clinicaltrials.gov identifier NCT02911077).¹⁹ Participants were recruited based on admission to the National Institutes of Health (NIH) Clinical Center (CC) alcohol rehabilitation unit and satisfaction of protocol eligibility criteria. If admitted and appearing to satisfy eligibility criteria, potential participants were approached by study staff to discuss protocol details and the option to participate. Data were collected at the NIH CC in Bethesda, MD from September 2016 to September 2017 and the study was funded by the NIH Intramural Research Program. The protocol was approved by the NIH Institutional Review Board. Informed consent was obtained from all participants prior to study initiation and procedures were carried out in accordance with Institutional Review Board regulations.

Participants were males and females at least 18 years of age who were admitted as inpatients to the NIH CC seeking treatment for AUD. Individuals who had a Body Mass Index (BMI) ≥ 30 kg/m², active uncontrolled gastrointestinal (GI) disorder (including inflammatory bowel disease, ulcerative colitis, Crohn’s disease, and/or infectious gastroenteritis, colitis, or gastritis), history of bowel resection or major GI surgery in the past five years, were enrolled in any other investigational study that may affect the microbiome, were taking antibiotics, corticosteroids, immunosuppressive or cytotoxic agents, and/or were taking supplemental probiotics at the time of the study or up to one month prior were excluded from the parent microbiome study.

Demographic variables

Self-reported age, sex, race, ethnicity and education level were obtained from medical records at the time of inpatient admission. All participants met the DSM-5 criteria for AUD. Severity of alcohol dependence was measured by self-report on the Alcohol Dependence Scale (ADS), with possible scores ranging from 0–47 and higher scores indicating more severe dependence.²⁰ Height (cm) and weight (kg) were used to calculate BMI and obtained from medical records as measured at inpatient admission using standardized methodology with a digital scale (Scale-tronix, Wheaton, IL) and stadiometer.

Dietary variables

While inpatient, all participants self-selected meals and optional snacks from the NIH CC room service menu. Patients were allowed to order freely from the menu for breakfast, lunch, dinner and one snack, but were capped at a maximum of eight items per tray and limits were placed on certain menu categories, such as two entrees, two beverages and one dessert at each meal. Across all meals, fruits and vegetables could be ordered freely within the eight-item total. Foodservice Suite, version 11.7.100, was used by the NIH CC foodservice section to process patient selections and produce patient trays.²¹ All meals and snacks were delivered with a ticket printout from Foodservice Suite listing the food and beverage items provided, as well as their portion sizes. At the time of intake, participants recorded which provided items were consumed and the amount of each item consumed. They were also asked to record any foods and beverages eaten from other sources and not provided by the CC foodservice section. These dietary records were collected by trained nutrition staff who reviewed them with participants, checking for completeness and probing for additional details as needed. This process is akin to food record methodology, which has been assessed for validity, and utilizes an open-ended instrument to self-report all foods and beverages consumed in real time.²²

To determine nutrient and food group intake, recorded dietary intake was then coded into Nutrition Data Systems for Research (NDSR) 2016–2017.²³ A coding manual was created to reflect standardized procedures for coding food products unique to the room service menu and to ensure consistency in this process. For purposes of analysis, intake data from day 1 of admission was not included as it did not reflect a full day of dietary intake. As available, intake from day 2 through day 7 of admission, and for 2 days prior to provision of a stool sample during the 2^nd, 3^rd and 4^th weeks of admission were coded and analyzed. To describe dietary intake over protocol participation, daily intake was averaged over all weeks for energy, macronutrients, micronutrients and food groups. Data on dietary supplement use was not collected and thus not included in nutrient totals. Healthy Eating Index (HEI)-2015 score was calculated for each participant using the simple HEI scoring algorithm method for when more than one day of dietary intake data is available. The HEI-2015 total score is the sum of 13 component scores, 9 of which are adequacy components, for which higher intake leads to a higher score, and 4 of which are moderation components, for which higher intake leads to a lower score.²⁴ The total score is used to assess diet quality, specifically examining the degree to which intake aligns with the Dietary Guidelines for Americans (DGA) 2015–2020.^24,25

Daily intake of select micronutrients of clinical interest was averaged for each AUD participant and compared to age and sex-specific Estimated Average Requirements (EAR) using the cut-point method.²⁶ To assess energy balance, individualized energy needs were estimated using the Mifflin-St. Jeor equation with a standard 1.4 activity factor.²⁷ For analyses comparing intake within the AUD sample across sexes, nutrients were energy-adjusted.

Statistical analysis

All data manipulation, figure generation and statistical analysis was conducted using the JMP^™ Version 14 Data Discovery Statistical Software.²⁸ The following comparisons were tested using the non-parametric Wilcoxon signed-rank test: intake differences between sex, intake differences between start and end of protocol, sex-specific differences between individual estimated energy needs versus actual intake, average weight change (last weight minus first weight) within sex between the start and end of protocol participation. Statistical significance was set at p<0.05.

Results

Participants

Fifty-three participants were screened for protocol inclusion; of whom 10 were not eligible due to BMI ≥30 kg/m², 15 did not meet other inclusion criteria and 5 refused study participation. Twenty-three participants enrolled, one of whom left the patient care unit less than 24 hours after admission and was excluded from all analyses. Of the 22 participants that were included in this secondary analysis, two participants discontinued participation prior to the 3^rd week of the protocol and two additional participants discontinued participation prior to the 4^th week. Thus, any tests examining change over time only included participants with data at the specified timepoint. The majority of participants were male (64%). Participants had an average age of 46.3 ± 13.0 (mean ± SD) years with a BMI of 23.9 ± 2.5 kg/m² and ADS score of 21.1 ± 6.1. See Table 1 for additional participant characteristics.

Table 1.

Characteristics of 22 adults with alcohol use disorder who participated in a four-week observational protocol during an inpatient treatment program at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017

	Mean ± SDa or Number (%)
Age (years)	46.3 ± 13.0
BMIb (kg/m2)	23.9 ± 2.5
Sex
Female	8 (36.3%)
Male	14 (63.6%)
Race
White	13 (59.1%)
African-American	6 (27.3%)
Multi-Racial	2 (9.1%)
Unknown	1 (4.5%)
Ethnicity
Hispanic or Latino	1 (4.5%)
Not Hispanic or Latino	21 (95.5%)
Education
< 12 years	8 (36.3%)
≥ 12 Years	14 (63.6%)
ADSc	21.1 ± 6.1

^aStandard Deviation

^bBody mass index

^cAlcohol Dependence Scale. Possible scores range from 0–47 with higher scores indicating more severe alcohol dependence.

Dietary intake of AUD participants

During protocol participation, study participants consumed an average energy intake of 2665 kcal/d, consisting of 45.9% carbohydrate, 34.9% fat and 19.1% protein, as shown in figure 1. Intake of macronutrients did not differ between males and females when energy adjusted (data not shown). Energy and macronutrient intake did not differ between week one and week four of the admission (n =18 for this analysis, data not shown). Intake of other dietary variables of interest are found in Table 2. HEI-2015 total and component scores are provided in table 3. Diet quality, as assessed by HEI-2015 score, was not statistically compared, but appeared to be similar between the AUD participants (62.3) and the overall US population (58).²⁹ Percent of participants with adequate intakes of select micronutrients of clinical interest, defined as intake at or above age and sex-specific EARs³⁰, are shown in Figure 2.

Figure 1.

Average energy and macronutrient intake of 22 adults with alcohol use disorder during participation in a four-week observational protocol at an inpatient treatment program at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017

Table 2.

Average intake of dietary variables of 22 adults with alcohol use disorder during participation in a four-week observational protocol at an inpatient treatment program at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017

Dietary Variable	Mean ± SDa
Total Dietary Fiber (grams/day)	25.8 ± 11.6
Total Sugar (grams/day)	156.7 ± 73.0
Added Sugar (grams/day)	88.9 ± 62.9
Sodium (mg/day)	4786.4 ± 1817.7
Omega-3 Fatty Acids (grams/day)	3.2 ± 1.6

^aStandard Deviation

Table 3.

Average Healthy Eating Index (HEI)-2015 component scores of 22 adults with alcohol use disorder during participation in a four-week observational protocol at an inpatient treatment program at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017

HEI-2015 Score	Mean ± SDa
HEI-2015 Total Score	62.3 ± 11.1
HEI-2105 Adequacy Components
Total Vegetablesb	4.7 ± 0.6
Greens and Beansb	4.5 ± 1.0
Total Fruitb	2.9 ± 1.4
Whole Fruitb	3.3 ± 1.5
Whole Grainsc	3.3 ± 1.6
Dairyc	6.5 ± 2.6
Total Protein Foodsb	4.9 ± 0.3
Seafood and Plant Proteinsb	3.9 ± 1.6
Fatty Acidsc	5.6 ± 3.1
HEI-2015 Moderation Components
Sodiumc	2.8 ± 2.4
Refined Grainsc	7.9 ± 2.1
Added Sugarsc	6.8 ± 2.7
Saturated Fatsc	5.2 ± 3.1

^aStandard Deviation

^bMaximum of 5 points for given component score

^cMaximum of 10 points for given component score

Figure 2.

Percent of 22 adults with alcohol use disorder with dietary intakes that meet or exceed the Estimated Average Requirements for select micronutrients while participating in a four-week observational protocol during an inpatient treatment program at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017

Figure 3 shows intake of select food groups by servings per day in the AUD participants. Participants in this sample consumed an average of 5.5 servings of vegetables daily. When types of vegetables included in this count were queried, potatoes and fried potatoes only accounted for less than one vegetable serving per day (data not shown). Additionally, though intake of total grains was 6.6 servings per day, intake of whole grains was only 1.1 serving per day.

Figure 3.

Average intake of select food groups in 22 adults with alcohol use disorder during participation in a four-week observational protocol at an inpatient treatment program at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017

Data presented as mean intake of servings per day +/− standard error of the mean.

^aLean animal-based proteins include lean beef, lean pork, lean cold cuts, lean fish and shellfish.

^bNon-meat proteins include eggs and egg substitutes, legumes, nuts and seeds, nut and seed butters, and meat alternatives (i.e., tofu, tempeh).

Intake and energy balance

To explore energy balance in participants throughout protocol participation, energy intake was compared to estimated energy needs. Figures 4 A and B show that females tended to consume a similar amount of energy to their estimated energy needs (p = 0.834), whereas males tended to consume more than their estimated needs (p=0.003).

Figure 4.

Comparison of estimated energy needs as calcuated by Mifflin-St. Jeor equation and average energy intake of 22 adults with alcohol use disorder during participation in a four-week observational protocol at an inpatient treatment program at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017

The box extends from the 25^th percentile to the 75^th percentile of data, with the distance between representing the interquartile range of data. The median is marked within the box as the red horizonal line. Whiskers represent the minimum and maximum points of the data. Individual data points are represented by black dots. Mid-line of the green diamond represents the mean and top and bottom represent the 95% confidence intervals.

Despite the observation that males tended to overconsume energy during protocol participation, when change in body weight in males was assessed from first to last weight, for those who had at least 3 weeks of data (n=11), there was no change (p=0.082). Females with data for at least 3 weeks (n=7) also showed no significant change in body weight (p=0.313). When weight change on an individual level was assessed, there was notable variability in weight gain and loss in females, but in males, one participant’s weight loss appeared to be an outlier (Figure 5). When this male participant was removed from analysis, males gained 2.67 ± 1.84 kg over the period of protocol participation (p=0.006).

Figure 5.

Change in weight (last weight-first weight) over length of participation in a four-week observational protocol for eleven male and seven female adults with alcohol use disorder during an inpatient treatment program at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017

Discussion

Despite a lack of precise quantification of dietary intake in patients with AUD undergoing treatment, literature suggests that patients with AUD undergoing treatment adopt unhealthful diets and experience undesirable weight gain.^11,12 This emphasis in the literature may in part be due to the extensive work on the overlap of neurological pathways that regulate pleasure, reward and motivation for alcohol and food as well as the interactions of appetite-regulating hormones involved in hunger and craving and the resulting hypothesis of “addiction transfer” between alcohol and highly palatable foods.^10,16 While this data is representative of a small sample in one specific AUD treatment program focused on clinical research, it is consistent with previously published results quantifying energy and macronutrient intake during treatment^17,18 and contradicts the idea that dietary intake of patients with AUD undergoing treatment is grossly unhealthy.^11,12

On the contrary, the dietary intake of the AUD sample included in this analysis reflected a macronutrient distribution within the ranges advised by the Dietary Reference Intakes (45–65% carbohydrate, 10–35% protein and 20–35% fat).³¹ Assessing diet quality using HEI-2015 scores suggests that while AUD participants are not meeting the 2015–2020 DGA, adherence to the guidelines is not dissimilar from that of a nationally representative sample.^25,29 In fact, if assessed qualitatively using a graded approach, adherence of AUD participants to the DGA would reflect a “D”, while that of a nationally representative American sample would reflect an “F”.^24,29 Of note, the HEI-2015 component scores needing most improvement within the AUD sample are sodium (score of 2.8 out of possible 10), whole grains (3.3 out of 10) and saturated fat (5.2 out of 10).

When assessing micronutrient intake, adequacy ranged from 41–100% of the sample meeting the EAR. When an acceptable sample adequacy of 80% was applied³², the AUD sample met this threshold for intakes of 10 of the 16 selected nutrients of clinical interest. The 6 micronutrients that fell below this 80% threshold were folate, vitamin C, calcium, magnesium, vitamin E and vitamin D. With the exception of folate, underconsumption of these same micronutrients has been identified in the diets of the overall U.S. population per the 2015–2020 DGA.²⁵ Evaluating the intake of micronutrients is of particular clinical importance in an inpatient program for treatment-seeking individuals with AUD, as malnutrition, nutrient deficiencies and associated comorbidities have been reported in patients with AUD.¹³

A diet rich in fruits and vegetables likely contributed substantially to the micronutrient intake observed in AUD participants. Combined, participants consumed on average almost 8 servings of fruits and vegetables per day (2.5 servings of fruit and 5.5 servings of vegetables). Potatoes and fried potatoes are frequently consumed by Americans³³ and have been reported to be a significant contributor to energy and nutrient intake³⁴ and proportion of overall vegetable intake.³⁵ However, results of this analysis showed that potatoes and fried potatoes combined accounted for less than 1 vegetable serving per day, supporting the finding that the AUD participants chose a diet plentiful in nutrient-rich vegetables during AUD treatment. In fact, with each serving calculated as ½ - 1 cup of a given fruit or vegetable depending on the item, the AUD participants potentially meet the 2015–2020 DGA recommendation for 2 cups of fruits per day (2.5 servings equates to 1 ¼–2 ½ cups fruit) and 3 ½ cups of vegetables per day (5.5 servings equates to 2 ¾–5 ½ cups vegetables).²⁵ Conversely, only 1.1 servings of whole grains were consumed out of 6.6 servings of total grains, falling short of the recommendation for approximately half of grains consumed to come from whole grain sources.²⁵ This may indicate that there was a less than optimal number of whole grain options available on the room service menu and could be an area for future focus.

Results of analyses exploring energy balance in this sample showed that while males consumed more than their estimated energy needs, this difference was not found for females. This discrepancy in findings between males and females may reflect the general tendency for females to be more interested in and aware of nutrition, and often more calorie conscious.³⁶ When the male participant whose weight loss was considered an outlier was removed from analysis, the overconsumption of energy compared to estimated energy needs led to weight gain amongst males over the course of the study. The observed weight gain and excess energy intake in males is consistent with other published findings,^11,12,17 however a more complex scenario emerged in this sample when considering the clinical picture of the outlier who lost 7 kg. This individual experienced acute pancreatitis during the initial days of protocol participation and subsequently had several days of transitional diet orders which affected acute calorie intake and energy balance over the entire protocol period. Acute pancreatitis is a recognized complication of AUD⁵ and is just one example of a potential clinical scenario affecting weight trajectories in this population during detoxification.

Indeed, variability in weight trends during detoxification from substance abuse has been identified in previous literature that showed about 21% of patients gained at least 5% of their body weight in 1 month, while another 22% of patients lost at least 5% of their body weight over a one-month timespan.³⁷ The present analysis observed a similar divergent response in weight trajectories amongst individual females, with a number of subjects losing weight and a number gaining weight. It is likely that specific features of the patient sample and treatment program may influence weight changes during this period. Unlike other research examining weight trends in this population^11,17, BMI ≥ 30 kg/m² was an exclusion criterion for the parent study of the microbiome and it is possible that this cap affected the range and magnitude of weight change observed in this analysis. Nonetheless, the finding that individual weight change during detoxification is not consistent in either direction (weight gain or weight loss), highlights the need to assess the individual and provide a personalized approach to nutrition care.

When interpreting the findings of this study one must consider that the inpatient treatment environment likely influenced food choices of the AUD protocol participants in two ways. First, the room service menu was robust, providing a wide variety of both healthy and energy dense food and beverage items. Though participants were allowed to eat food purchased from outside sources, the previously described limits on food items from the inpatient food service program, and the limited access to outside foods may have encouraged healthier food selections. Second, patients had access to weekly nutrition education classes led by an RDN, where topics were tailored to encourage healthful eating behaviors in patients undergoing AUD treatment. Increased nutrition knowledge gained from these classes may have helped patients choose healthier foods during their admission.⁸

Limitations of this analysis should be considered when assessing observed results. Findings reflect intake habits of a small sample of non-obese participants as they were residing in an inpatient treatment facility with a specific room service-based foodservice system; thus, results may not be generalizable. It should also be noted that dietary assessment in this study covers only the first four weeks of treatment and it is possible that nutrient needs, psychological factors affecting food consumption and dietary intake patterns differ as patients continue to undergo treatment. The inpatient treatment period is not representative of life after or outside of treatment. Important factors that can alter food choice at home, such as time and skill needed for meal planning and preparation were not applicable for participants in this study. Additionally, participants were actively and voluntarily seeking treatment, which may indicate increased motivation for behavior change in these participants, compared with individuals with AUD who are not seeking treatment. Finally, when considering findings related to energy and weight balance, needs were estimated by equation, rather than measured using indirect calorimetry and physical activity data was not available for analyses.

Despite these limits, the strengths of this research are also notable. Findings add to a gap in existing literature and are particularly relevant given advances in the study of mechanisms by which diet during treatment may affect health.^8–10 Unlike many prior studies that have assessed diet during treatment,^11–14 this analysis utilized methodology that captured dietary intake in real time and incorporated a review by trained nutrition professionals. Though limited by the nature of self-report, this method of dietary assessment provided data rich in detail and maximized for precision. In addition, a robust food and nutrient database, NDSR, was used to quantify nutrient and food group intake.²³ Lastly, though this study focused only on the initial four weeks of treatment, capturing this degree of detailed diet data over this period of time is quite unique. While follow-up dietary assessment would be of benefit, collected data did cover the most initial phases of detoxification, during which researchers could hypothesize that usual intake may be most affected.

Conclusions

To help guide clinical practice recommendations, future studies should use precise and in-depth assessment methods to quantify dietary intake during AUD treatment in larger samples over longer time periods, as well as in treatment programs in different settings. In the meantime, this analysis suggests that dietary intake during an inpatient AUD treatment program is certainly variable, but is not without merit. Results show that there are aspects of diet in need of improvement and strategies to address these and increase adherence to the DGA are warranted. However, practitioners should provide individualized care and avoid assumptions about dietary intake for all patients entering AUD treatment, as females in this study did not overconsume calories and diet quality based on macronutrient, micronutrient and fruit and vegetable intake, was not ubiquitously poor as suggested by prior literature that looks at addiction transfer.

Research Snapshot.

Research Question:

What is the dietary intake of people with Alcohol Use Disorder (AUD) during an inpatient treatment program and how does that align with estimated energy needs and recommendations for macro- and micronutrient intake?

Key Findings:

In this observational study of 22 participants, quantification of dietary intake that was self-selected from a room service menu revealed intake consistent with the Dietary Reference Intakes for most macro- and micronutrients, with average energy intake of 2665 kcal/d, consisting of 45.9% carbohydrate, 34.9% fat and 19.1% protein. Energy intake of females was similar to estimated needs, whereas males tended to consume more than their estimated needs leading to weight gain.